Stress Propagation in Human-Robot Teams Based on Computational Logic Model

TL;DR

This paper introduces a novel composite model for monitoring and understanding stress propagation in human-robot teams, combining computational logic, decision-making, and SIS-based stress dynamics to improve mission success in stressful scenarios.

Contribution

It presents a new integrated model that combines computational logic, decision processes, and SIS-based stress propagation for human-robot team analysis.

Findings

The model effectively describes stress states in small teams.

It predicts how stress propagates among team members.

The approach is adaptable to human-machine teaming scenarios.

Abstract

Mission teams are exposed to the emotional toll of life and death decisions. These are small groups of specially trained people supported by intelligent machines for dealing with stressful environments and scenarios. We developed a composite model for stress monitoring in such teams of human and autonomous machines. This modelling aims to identify the conditions that may contribute to mission failure. The proposed model is composed of three parts: 1) a computational logic part that statically describes the stress states of teammates; 2) a decision part that manifests the mission status at any time; 3) a stress propagation part based on standard Susceptible-Infected-Susceptible (SIS) paradigm. In contrast to the approaches such as agent-based, random-walk and game models, the proposed model combines various mechanisms to satisfy the conditions of stress propagation in small groups. Our core approach involves data structures such as decision tables and decision diagrams. These tools are adaptable to human-machine teaming as well.